The global navigation satellite system (GNSS), represented by global positioning systems (GPS), is widely used in various civil and military fields and represents an essential basis for space-time information services. However, the radar signals partially overlap with the frequency band of satellite navigation signals, seriously affecting the normal reception of weak satellite navigation signal power. To further improve anti-jamming with sweep interference in the time domain, this paper focuses on the sweep interference scenario, studies the influence of the sweep interference on time-domain-adaptive anti-jamming, and proposes a timing reset based on the adaptive filter. The proposed method can effectively deal with the influence of sweep interference on time-domain-adaptive anti-jamming and can suppress interference and protect signals at the same time. Simulation experiments verify the effectiveness of the anti-jamming method proposed in this paper. Under the typical simulation scenarios, the influence time of the frequency sweep interference on the navigation signal is less than 1 m when the timing reset period is 1 m, which is significantly reduced compared to traditional methods. The proposed anti-jamming method is of great significance for improving the survivability of satellite navigation receivers in sweep interference scenarios.

A road network is a critical aspect of both urban planning and route recommendation. This article proposes an efficient approach to build a fine-grained road network based on sparsely sampled private car trajectory data under complex urban environment. In order to resolve difficulties introduced by low sampling rate trajectory data, we concentrate sample points around intersections by utilizing the turning characteristics from the large-scale trajectory data to ensure the accuracy of the detection of intersections and road segments. In front of complex road networks including many complex intersections, such as the overpasses and underpasses, we first layer intersections into major and minor one, and then propose a simplified representation of intersections and corresponding computable model based on the features of roads, which can significantly improve the accuracy of detected road networks, especially for the complex intersections. In order to construct fine-grained road networks, we distinguish various types of intersections using direction information and detected turning limit. To the best of our knowledge, our road network building method is the first time to give fine-grained road networks based on low-sampling rate private car trajectory data, especially able to infer the location of complex intersections and its connections to other intersections. Last but not the least, we propose an effective parameter selection process for the Density-Based Spatial Clustering of Applications with Noise based clustering algorithm, which is used to implement the reliable intersection detection. Extensive evaluations are conducted based on a real-world trajectory dataset from 1,345 private cars in Futian district, Shenzhen city of China. The results demonstrate the effectiveness of the proposed method. The constructed road network matches close to the one from a public editing map OpenStreetMap, especially the location of the road intersections and road segments, which achieves 92.2% intersections within 20m and 91.6% road segments within 8m.

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